Sign in

username:

password:



Not a member?

Search compdsp



Search tips

comp.dsp by Keywords

Adaptive Filter | ADPCM | ADSP | ADSP-2181 | Aliasing | AMR | Anti-Aliasing | ARMA | Autocorrelation | AutoCovariance | Beamforming | Bessel | Blackfin | Butterworth | C6713 | CCS | Chebyshev | CIC Filter | Circular Convolution | Code Composer Studio | Comb Filter | Compression | Convolution | Cross Correlation | DCT | Decimation | Deconvolution | Demodulation | DM642 | DSP Boards | DSP/BIOS | DTMF | Echo Cancellation | Equalization | Equalizer | ETSI | EZLITE (Ez-kit Lite) | FFT | FFTW | FIR Filter | Fixed Point | FSK | G.711 | G.723 | G.729 | Gaussian Noise | Goertzel | GPIO | Hilbert Transform | IFFT | IIR Filter | Interpolation | Invariance | JTAG | Kalman | Laplace Transform | Levinson | LPC | McBSP | MIPS | Modulation | MPEG | Multirate | Notch Filter | Nyquist | OFDM | Oversampling | Pink Noise | Pitch | PLL | Polyphase | QAM | QDMA | Quantization | Quantizer | Radar | Random Noise | Reed Solomon | Remez | Resampling | RTDX | Sampling | Sharc | TI C6711 | Undersampling | Viterbi | Wavelets | White Noise | Wiener Filter | Windowing | XDS510PP | Z Transform

Sponsor

Industry's highest performing at the lowest power DSPs now as low as $5.00*
Start development today!
*volume pricing for 10ku

Discussion Groups

Free Online Books

See Also

Embedded SystemsFPGAElectronics

Discussion Groups | Comp.DSP | why is an image non-stationary?

There are 11 messages in this thread.

You are currently looking at messages 10 to 11.


Re: why is an image non-stationary? - Peter Kootsookos - 2004-11-24 16:58:00

Gordon Sande <g...@worldnet.att.net> wrote 

> Position invariance would mean that a seascape would have the same
> properties as an urban image. If you think not then you have given
> up the Fourier assumptions. The things folks will agree on tend to
> suggest Haar analysis or perhaps wavelets.

I actually think it's a little different from the way you say it.  I
don't think it's that the stationarity problem occurs between
different images, it's that the statistics of an image change _within_
a given image.

For example, the pixel values of the sea in a seascape would have
different mean and standard deviation from the pixel values of the
beach in the same seascape.

That's why images can be thought of as non-stationary.

For what it's worth, I really don't think that stationarity says
anything about whether the Fourier transform can or can't be used.  Of
course it can be used; how you interpret it might be a problem, but
many non-stationary problems (e.g. speech, sonar) have used the
Fourier transform to good effect.

Ciao,

Peter K.
______________________________
New DSP Code Snippets Section now Live.   Learn more about the reward program for contributors here.

previous | 1 | 2